mitosRNAseq Workflow

Summary

This workflow demonstrates the mitosRNAseq pipeline which identifies several types of small RNA from sequencing data from short reads of <50 nt. The following method collects sequence-based counts, sequence locations, genomic features, and annotations. The data we use is from killifish embyros anoxia experiments.

The step-by-step workflow we follow is illistrated below and a detailed method to perform small RNA seq on an academic HPC is decsribed in this document.

The following table shows the contents of the file samples.csv that contains metadata for our samples. We start with a total of 40 fastq files.

Sample Table

#	Sample	Condition	Stage	Input files
1	31_4d_t0	4d_t0	4	31_4d_t0.fastq.gz
2	32_4d_t0	4d_t0	4	32_4d_t0.fastq.gz
3	33_4d_t0	4d_t0	4	33_4d_t0.fastq.gz
4	34_4d_t0	4d_t0	4	34_4d_t0.fastq.gz
5	35_4d_4hrA	4d_4hrA	4	35_4d_4hrA.fastq.gz
6	36_4d_4hrA	4d_4hrA	4	36_4d_4hrA.fastq.gz
7	37_4d_4hrA	4d_4hrA	4	37_4d_4hrA.fastq.gz
8	38_4d_4hrA	4d_4hrA	4	38_4d_4hrA.fastq.gz
9	39_4d_24hrA	4d_24hrA	4	39_4d_24hrA.fastq.gz
10	40_4d_24hrA	4d_24hrA	4	40_4d_24hrA.fastq.gz
11	41_4d_24hrA	4d_24hrA	4	41_4d_24hrA.fastq.gz
12	42_4d_24hrA	4d_24hrA	4	42_4d_24hrA.fastq.gz
13	43_4d_2hrR	4d_2hrR	4	43_4d_2hrR.fastq.gz
14	44_4d_2hrR	4d_2hrR	4	44_4d_2hrR.fastq.gz
15	45_4d_2hrR	4d_2hrR	4	45_4d_2hrR.fastq.gz
16	46_4d_2hrR	4d_2hrR	4	46_4d_2hrR.fastq.gz
17	47_4d_24hrR	4d_24hrR	4	47_4d_24hrR.fastq.gz
18	48_4d_24hrR	4d_24hrR	4	48_4d_24hrR.fastq.gz
19	49_4d_24hrR	4d_24hrR	4	49_4d_24hrR.fastq.gz
20	50_4d_24hrR	4d_24hrR	4	50_4d_24hrR.fastq.gz
21	1_12d_t0	12d_t0	12	1_12d_t0.fastq.gz
22	2_12d_t0	12d_t0	12	2_12d_t0.fastq.gz
23	5_12d_t0	12d_t0	12	5_12d_t0.fastq.gz
24	6_12d_t0	12d_t0	12	6_12d_t0.fastq.gz
25	10_12d_4hrA	12d_4hrA	12	10_12d_4hrA.fastq.gz
26	11_12d_4hrA	12d_4hrA	12	11_12d_4hrA.fastq.gz
27	12_12d_4hrA	12d_4hrA	12	12_12d_4hrA.fastq.gz
28	7_12d_4hrA	12d_4hrA	12	7_12d_4hrA.fastq.gz
29	13_12d_24hrA	12d_24hrA	12	13_12d_24hrA.fastq.gz
30	16_12d_24hrA	12d_24hrA	12	16_12d_24hrA.fastq.gz
31	17_12d_24hrA	12d_24hrA	12	17_12d_24hrA.fastq.gz
32	18_12d_24hrA	12d_24hrA	12	18_12d_24hrA.fastq.gz
33	19_12d_2hrR	12d_2hrR	12	19_12d_2hrR.fastq.gz
34	21_12d_2hrR	12d_2hrR	12	21_12d_2hrR.fastq.gz
35	22_12d_2hrR	12d_2hrR	12	22_12d_2hrR.fastq.gz
36	23_12d_2hrR	12d_2hrR	12	23_12d_2hrR.fastq.gz
37	25_12d_24hrR	12d_24hrR	12	25_12d_24hrR.fastq.gz
38	28_12d_24hrR	12d_24hrR	12	28_12d_24hrR.fastq.gz
39	29_12d_24hrR	12d_24hrR	12	29_12d_24hrR.fastq.gz
40	30_12d_24hrR	12d_24hrR	12	30_12d_24hrR.fastq.gz

QC for raw reads

Quality Control (QC) is done by running fastQC on raw reads to evaluate how good (or bad) our data looks.

Install software for Quality Check

conda

Set up a virtual environment in conda that has all of the Quality Control (QC) tools. We are using Miniconda. For creating sharable environments, create .yaml files for each environment you’re making.

conda create -n condafastqc fastqc
conda activate condafastqc
conda install -n multiqc

tmux

For almost every long-ish process, we use tmux to run tasks in a detached terminal window that can keep running if we want to log out or if we accidently log out.

tmux new -s fastqc

Executing

Creating a new file called fastqcr.R that has the script to run fastqc through R. We use this script to automate the process for a list of files in a directory. Running commands through various scripts also keeps a record of our commands.

if (!require("fastqcr")) {
        install.packages("fastqcr", repos="http://cran.us.r-project.org")
        library(fastqcr)
} else {library(fastqcr)}
fastqc(fq.dir="/path/to/directory/init_fastq_files",
      qc.dir="/path/to/directory/fastqc/init_fastqc",
      threads=20)

Running the script with the following command:

Rscript fastqcr.R

Press Ctrl+B and then D for detaching this tmux window. You can log out from your remote computer (HPC) and close your terminal window now while this process keeps running on your remote computer. To get back to this window, log in to your remote computer, and use the command tmux a -t fastqc. F or more information, check tmux.

We will get a fastqc HTML file in the output fastqc directory for each fastq sequence in the input init_fastq_files directory.

FastQC Output

General Statistics from multiqc

Sample	PercentDups	PercentGC	BPMedianReadLength	MillionsSeqs
10_12d_4hrA	94.1	51	36	12.6
11_12d_4hrA	92.7	51	36	16.2
12_12d_4hrA	87.4	52	36	9.0
13_12d_24hrA	90.5	53	36	8.5
16_12d_24hrA	91.1	50	36	7.9
17_12d_24hrA	92.4	50	36	29.9
18_12d_24hrA	84.6	52	36	11.8
19_12d_2hrR	87.6	52	36	11.6
1_12d_t0	86.4	51	36	8.7
21_12d_2hrR	95.4	54	36	9.8
22_12d_2hrR	80.5	52	36	6.7
23_12d_2hrR	89.3	52	36	7.8
25_12d_24hrR	88.1	53	36	13.5
28_12d_24hrR	91.1	55	36	5.9
29_12d_24hrR	91.4	54	36	8.3
2_12d_t0	91.5	54	36	8.3
30_12d_24hrR	79.9	53	36	12.8
31_4d_t0	79.3	52	50	11.9
32_4d_t0	80.1	53	50	11.6
33_4d_t0	86.3	51	50	13.7
34_4d_t0	82.9	52	50	15.2
35_4d_4hrA	88.4	52	50	11.0
36_4d_4hrA	87.0	51	50	13.8
37_4d_4hrA	84.7	52	50	15.6
38_4d_4hrA	84.4	51	50	15.3
39_4d_24hrA	79.7	53	50	12.4
40_4d_24hrA	74.4	51	50	12.9
41_4d_24hrA	79.3	52	50	9.7
42_4d_24hrA	85.6	53	50	16.7
43_4d_2hrR	88.3	52	50	13.0
44_4d_2hrR	81.5	44	101	11.0
45_4d_2hrR	87.9	44	101	13.8
46_4d_2hrR	88.9	52	50	16.9
47_4d_24hrR	72.8	45	101	14.6
48_4d_24hrR	83.2	53	50	13.4
49_4d_24hrR	87.1	51	50	22.8
50_4d_24hrR	88.9	41	101	14.8
5_12d_t0	81.3	53	36	11.2
6_12d_t0	81.7	53	36	13.6
7_12d_4hrA	81.4	52	36	10.5

Sequence Counts for each sample

Sequence Counts

The mean quality scores per base in the read

Per Base Quality

The average quality scores per sequence

Per Sequence Quality

The GC content per sequence

GC conetent

Sequence Duplication

Duplication in each sequence

Overrepresented sequences

Total overrepresented sequences found in each sample

Adapter Content

Adapters found at sequence positions

Overall Pass/Fail

Checking if each sample passes or fails in various meteris

Sequence length distribution

All samples had sequences of a single length (50bp , 36bp , 101bp).

Trimming

Trimming is done with Trimmomatic in our case, however, other tools like cutadapt or fastp may also be used. This tool runs through the following bash script

FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/copied_old_infiles/init_fastq_files/*.fastq.gz"
for f in $FILES
    do
        b=`basename $f`
        echo Running trimming for the file $b

        c=${b::-9}
        o="$c.trim.fastq"
    log="$c.out.log"
        c="$c.log.txt"
        java -jar /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/trim/Trimmomatic-0.39/trimmomatic-0.39.jar SE -threads 20 -phred33 -trimlog $c $f $o ILLUMINACLIP:/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/copied_old_infiles/adapter_contamination_sequences_AR3.txt:2:30:5:1:true SLIDINGWINDOW:5:15 LEADING:20 TRAILING:20 MINLEN:15 2> $log
    done

Reads Surviving

Sample	TotalReads	SurvivingReads	PercentSurviving	DroppedReads	PercentDropped
10_12d_4hrA	12625447	4421375	35.0	8204072	65.0
11_12d_4hrA	16238016	8893201	54.8	7344815	45.2
12_12d_4hrA	9024387	7219072	80.0	1805315	20.0
13_12d_24hrA	8467459	5182199	61.2	3285260	38.8
16_12d_24hrA	7908381	6458185	81.7	1450196	18.3
17_12d_24hrA	29872147	17868683	59.8	12003464	40.2
18_12d_24hrA	11793810	8182929	69.4	3610881	30.6
19_12d_2hrR	11606761	9574385	82.5	2032376	17.5
1_12d_t0	8698124	6498924	74.7	2199200	25.3
21_12d_2hrR	9784686	3585294	36.6	6199392	63.4
22_12d_2hrR	6741253	4728213	70.1	2013040	29.9
23_12d_2hrR	7836111	4455897	56.9	3380214	43.1
25_12d_24hrR	13534542	8957294	66.2	4577248	33.8
28_12d_24hrR	5866632	2402889	41.0	3463743	59.0
29_12d_24hrR	8305061	2923930	35.2	5381131	64.8
2_12d_t0	8344087	6946381	83.2	1397706	16.8
30_12d_24hrR	12811149	8785705	68.6	4025444	31.4
31_4d_t0	11936271	10306761	86.3	1629510	13.7
32_4d_t0	11595360	7974434	68.8	3620926	31.2
33_4d_t0	13655749	10422782	76.3	3232967	23.7
34_4d_t0	15201813	11186661	73.6	4015152	26.4
35_4d_4hrA	11045591	7821501	70.8	3224090	29.2
36_4d_4hrA	13840101	9383343	67.8	4456758	32.2
37_4d_4hrA	15579833	12373672	79.4	3206161	20.6
38_4d_4hrA	15286687	12142640	79.4	3144047	20.6
39_4d_24hrA	12442304	9755623	78.4	2686681	21.6
40_4d_24hrA	12866355	11130799	86.5	1735556	13.5
41_4d_24hrA	9720710	8032689	82.6	1688021	17.4
42_4d_24hrA	16698310	12979297	77.7	3719013	22.3
43_4d_2hrR	12989273	9158318	70.5	3830955	29.5
44_4d_2hrR	10968948	8421811	76.8	2547137	23.2
45_4d_2hrR	13812376	6527219	47.3	7285157	52.7
46_4d_2hrR	16946884	12011097	70.9	4935787	29.1
47_4d_24hrR	14627707	12933658	88.4	1694049	11.6
48_4d_24hrR	13383720	10269804	76.7	3113916	23.3
49_4d_24hrR	22803428	18172334	79.7	4631094	20.3
50_4d_24hrR	14759393	7495120	50.8	7264273	49.2
5_12d_t0	11217968	7594023	67.7	3623945	32.3
6_12d_t0	13607922	9592402	70.5	4015520	29.5
7_12d_4hrA	10489257	6515199	62.1	3974058	37.9

QC for trimmed reads

Quality Control (QC) is done for the trimmed reads with fastQC. We activate the conda environment condafastqc for this process.

if (!require("fastqcr")) {
        install.packages("fastqcr", repos="http://cran.us.r-project.org")
        library(fastqcr)
} else {library(fastqcr)}
fastqc(fq.dir="/path/to/directory/trimmed_fastq_files",
      qc.dir="/path/to/directory/fastqc/trim_fastqc",
      threads=20)

Running the script with the following command:

Rscript fastqcr.R

FastQC Output

Plots for each FASTQC metric after trimming is shown below. All the adapters were removed and overall quality looks better.

General Statistics from multiqc

Sample	PercentDups	PercentGC	BPMedianReadLength	MillionsSeqs
10_12d_4hrA	89.4	50	22	4.4
11_12d_4hrA	90.3	49	22	8.9
12_12d_4hrA	88.7	51	22	7.2
13_12d_24hrA	88.4	50	22	5.2
16_12d_24hrA	91.9	48	22	6.5
17_12d_24hrA	91.2	48	22	17.9
18_12d_24hrA	84.9	50	22	8.2
19_12d_2hrR	88.5	51	22	9.6
1_12d_t0	86.7	49	22	6.5
21_12d_2hrR	90.5	47	22	3.6
22_12d_2hrR	80.9	50	22	4.7
23_12d_2hrR	86.9	49	22	4.5
25_12d_24hrR	86.6	50	22	9.0
28_12d_24hrR	84.3	50	22	2.4
29_12d_24hrR	83.9	50	22	2.9
2_12d_t0	93.8	53	22	6.9
30_12d_24hrR	78.7	51	22	8.8
31_4d_t0	80.8	49	22	10.3
32_4d_t0	77.1	50	22	8.0
33_4d_t0	86.5	48	22	10.4
34_4d_t0	81.7	51	22	11.2
35_4d_4hrA	86.5	50	22	7.8
36_4d_4hrA	82.7	49	22	9.4
37_4d_4hrA	84.8	51	22	12.4
38_4d_4hrA	84.2	48	22	12.1
39_4d_24hrA	79.1	51	23	9.8
40_4d_24hrA	73.6	50	27	11.1
41_4d_24hrA	79.0	49	22	8.0
42_4d_24hrA	85.3	50	22	13.0
43_4d_2hrR	86.9	50	22	9.2
44_4d_2hrR	82.1	50	20	8.4
45_4d_2hrR	80.0	50	20	6.5
46_4d_2hrR	88.4	48	22	12.0
47_4d_24hrR	73.3	48	26	12.9
48_4d_24hrR	82.6	52	22	10.3
49_4d_24hrR	87.9	49	22	18.2
50_4d_24hrR	84.4	49	20	7.5
5_12d_t0	81.2	50	22	7.6
6_12d_t0	82.7	51	22	9.6
7_12d_4hrA	79.5	50	22	6.5

Sequence Counts for each sample

Sequence Counts

The mean quality scores per base in the read

Per Base Quality

The average quality scores per sequence

Per Sequence Quality

The GC content per sequence

GC conetent

Sequence Duplication

Duplication in each sequence

Overrepresented sequences

Total overrepresented sequences found in each sample

Overall Pass/Fail

Checking if each sample passes or fails in various meteris

Sequence length distribution

length distribution of sequences

Alignment

First things first- create a conda environment that has bowtie, samtools, picard, and bedtools.

conda create -n condabowtie bowtie samtools picard bedtools

The trimmed reads are aligned to the reference genome using bowtie. To analyze reads mapping with mitochondrial genome, we perform the alignment in two steps.

1. Complete Genome (with mitochondrial genome)- with algenome.fa
2. Only the Mitochondrial genome which is extracted from genome.fa- almitogenome.fa

To begin alignment, we make bowtie indexes, this is done in two sets.

bowtie-index algenome.fa
bowtie-index almitogenome.fa

Alignment with genome

The alignment was done on the trimmed reads from previous section.

#!/bin/bash
FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/trim/*.fastq"
for f in $FILES
    do
        b=`basename $f`
        echo Running trimming for the file $b
        t=${b::-6}
        o="$t.sam"
        uo="$t.mapped.fq"
        echo Output file is $o
        bowtie \
     -p 20 \
         -t \
     -k 5 \
     --best \
     --strata \
     -e 99999 \
     -v 0 \
     -l 15 \
     --chunkmbs 2048 \
     -x /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/bowtie_index_alim/algenome \
     -q $f \
     --al $uo \
     --sam --no-unal > $o 2> $t.bowtie.log
    done
#done

The following stats show alignment QC.

Bowtie Alignment Stats with Genome

This table shows the number of reads aligned in each sample (MillionAlignedReads). It also shows the number of aligments of various reads that includes multimapped reads (MillionMappedReads).

Sample	PercentAligned	MillionAlignedReads	MillionMappedReads
10_12d_4hrA	73.2	3.2	8.0
11_12d_4hrA	73.6	6.5	15.4
12_12d_4hrA	70.4	5.1	12.4
13_12d_24hrA	69.6	3.6	8.7
16_12d_24hrA	77.5	5.0	10.3
17_12d_24hrA	75.8	13.5	31.4
18_12d_24hrA	73.7	6.0	15.0
19_12d_2hrR	71.9	6.9	16.8
1_12d_t0	73.3	4.8	11.1
21_12d_2hrR	77.8	2.8	5.3
22_12d_2hrR	66.2	3.1	7.4
23_12d_2hrR	73.2	3.3	7.8
25_12d_24hrR	69.7	6.2	15.0
28_12d_24hrR	65.3	1.6	3.7
29_12d_24hrR	70.9	2.1	5.0
2_12d_t0	76.9	5.3	13.1
30_12d_24hrR	65.5	5.8	13.8
31_4d_t0	62.0	6.4	11.8
32_4d_t0	73.5	5.9	12.2
33_4d_t0	80.6	8.4	15.0
34_4d_t0	70.5	7.9	15.8
35_4d_4hrA	71.5	5.6	9.2
36_4d_4hrA	62.1	5.8	8.3
37_4d_4hrA	69.1	8.6	16.4
38_4d_4hrA	76.1	9.2	15.4
39_4d_24hrA	79.0	7.7	17.2
40_4d_24hrA	52.3	5.8	10.9
41_4d_24hrA	63.0	5.1	8.8
42_4d_24hrA	66.6	8.6	15.2
43_4d_2hrR	71.2	6.5	11.7
44_4d_2hrR	64.6	5.4	11.8
45_4d_2hrR	65.7	4.3	9.5
46_4d_2hrR	71.2	8.6	13.8
47_4d_24hrR	80.1	10.4	24.4
48_4d_24hrR	51.8	5.3	10.7
49_4d_24hrR	72.3	13.1	23.2
50_4d_24hrR	79.4	6.0	13.4
5_12d_t0	66.2	5.0	11.9
6_12d_t0	67.5	6.5	15.5
7_12d_4hrA	65.6	4.3	10.1

Alignment with mitochondria

The alignment was done on the trimmed reads from previous section.

The following stats show alignment QC.

#!/bin/bash
FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/trim/*.fastq"   
for f in $FILES
    do
        b=`basename $f`
        echo Running alignment for the file $b
        t=${b::-6}
        o="$t.sam"
    uo="$t.mapped.fq"
        echo Output file is $o
        bowtie \
     -p 20 \
         -t \
     -k 10 \
     --best \
     --strata \
     -e 99999 \
     -v 0 \
     -l 15 \
     --chunkmbs 2048 \
     -x /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/bowtie_mito_index/almitogenome \
     -q $f \
     --al $uo \
     --sam --no-unal > $o 2> $t.bowtie.log
    done
#done

Bowtie Alignment Stats with Mitochondrial Genome

This table shows the number of reads aligned in each sample (MillionAlignedReads). It also shows the number of aligments of various reads that includes multimapped reads (MillionMappedReads).

Sample	PercentAligned	MillionAlignedReads	MillionMappedReads
10_12d_4hrA	0.34	0.01	0.02
11_12d_4hrA	0.47	0.04	0.04
12_12d_4hrA	0.41	0.03	0.03
13_12d_24hrA	0.50	0.03	0.03
16_12d_24hrA	0.52	0.03	0.03
17_12d_24hrA	3.25	0.58	0.59
18_12d_24hrA	6.22	0.51	0.52
19_12d_2hrR	0.92	0.09	0.09
1_12d_t0	1.25	0.08	0.08
21_12d_2hrR	0.79	0.03	0.03
22_12d_2hrR	4.43	0.21	0.21
23_12d_2hrR	2.68	0.12	0.12
25_12d_24hrR	4.72	0.42	0.43
28_12d_24hrR	0.32	0.01	0.01
29_12d_24hrR	4.08	0.12	0.12
2_12d_t0	0.25	0.02	0.02
30_12d_24hrR	8.18	0.72	0.73
31_4d_t0	4.99	0.51	0.52
32_4d_t0	10.12	0.81	0.82
33_4d_t0	1.99	0.21	0.21
34_4d_t0	6.72	0.75	0.77
35_4d_4hrA	0.43	0.03	0.03
36_4d_4hrA	0.59	0.06	0.06
37_4d_4hrA	1.10	0.14	0.14
38_4d_4hrA	5.67	0.69	0.70
39_4d_24hrA	8.32	0.81	0.83
40_4d_24hrA	7.77	0.87	0.88
41_4d_24hrA	7.50	0.60	0.61
42_4d_24hrA	2.13	0.28	0.28
43_4d_2hrR	0.61	0.06	0.06
44_4d_2hrR	1.00	0.08	0.09
45_4d_2hrR	4.69	0.31	0.31
46_4d_2hrR	1.07	0.13	0.13
47_4d_24hrR	16.68	2.16	2.19
48_4d_24hrR	0.18	0.02	0.02
49_4d_24hrR	1.49	0.27	0.28
50_4d_24hrR	2.22	0.17	0.17
5_12d_t0	6.11	0.46	0.47
6_12d_t0	6.47	0.62	0.63
7_12d_4hrA	5.39	0.35	0.36

Another QC that we could fo with the aligned read is with Picard. The following is the command line to do that:

FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alim_for_anno/alim_mito_anno/*sorted.bam"
for f in $FILES
    do
        b=`basename $f`
        echo Converting file $b
        o="$b.picard.txt"
        echo Output file is $o
    picard -Xmx5g CollectAlignmentSummaryMetrics I=$f O=$o R=/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/copied_old_infiles/GCF_001266775.1_Austrofundulus_limnaeus-1.0_genomic_andMITO.fna
    done
#done

Before we proceed, we need to sort the sam, convert it into bam and create a bam index (bai). This step ensures that we could store the alignments in acceptable binary format that takes less storage space and is compatible with subsequent steps.

FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alim_for_anno/alim_mito_anno/*.sam"
for f in $FILES
    do
        b=`basename $f`
        echo Converting $b to bam
        o=${b::-4}
        bam="$o.bam"
    sortedbam="$b.sorted.bam"
    
    echo Tmp file is $bam
        samtools view -b $f > $bam
    
        echo Output files are $sortedbam and its index
    samtools sort $bam -o $sortedbam
    samtools index $sortedbam
    echo Deleting Tmp file $bam 
    rm $bam 
    done
#done

Using aligned reads

We will create three files:

1. Summary information that has read ID, sequence, reference location, and number of alignments.
2. Exact location, genomic features of the reference location that the reads mapped to via bedtools.
3. Counting the readcounts from bam files.

FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alim_for_anno/alim_mito_anno/*.bam"
for f in $FILES
    do
        b=`basename $f`
        echo $b
        o=${b::-4}
        o="$o.summary.txt"
    input_file="$f"
    output_file="$o.summary.txt"

    # Process the input file with samtools and awk
    samtools view $f | awk '{print $1, $10, $3, $4, $15}' | sort | uniq > "$output_file"
    echo "Output file written $output_file"
    done

The result is the following data for each file. The sample 1_12d_t0 is shown.

FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alim_for_anno/alim_mito_anno/*.bam"
for f in $FILES
    do
        b=`basename $f`
        echo $b
        o=${b::-4}
        bed="$o.bed"
    out="$o.bedtools.txt"
    gff="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/mito_genome/GCF_001266775.1_Austrofundulus_limnaeus_MITOonly.gff"
    bedtools intersect -a $f -b $gff -wo -f 1 -bed > $bed
    awk '!seen[$4]++' $bed > tmp.csv
    rm $bed
    awk '{print $4, $21}' tmp.csv > $out
    rm tmp.csv
    echo "Output file written $out"
    done

The result of above command gives us the following data for each file. The sample 1_12d_t0 is shown.

FILES="/disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alim_for_anno/alim_mito_anno/*.bam"
for f in $FILES
    do
        b=`basename $f`
        echo $b
        o=${b::-4}
        o="$o.readcount.txt"
        echo Output file is $o
    # Count unique occurrences of column 10
    samtools view $f | awk '{print $10}'| sort | uniq -c > "$o"
    echo "Output written to $o"
    done

The result of above command gives us the following data for each file. The sample 1_12d_t0 is shown.

miRTrace

Running mirtrace with the following command.

mirtrace qc --species dre --config seq_address.txt

Looking at the results:

sports

Executing sports tool on mapped reads only. We use the following command to run:

#!/bin/bash
sports.pl -i seqs_alim_mito.txt \
-o /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alimnaeus/gen_input_fastq/SPORTS/out_sports/REDO/mito_mapped_reads \
-k \
-M 2 \
-p 20 \
-g /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/bowtie_mito_index/almitogenome \
-m /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alimnaeus/gen_input_fastq/SPORTS/sports1.1/db/Danio_rerio/miRBase_21/miRBase_21-dre \
-r /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alimnaeus/gen_input_fastq/SPORTS/sports1.1/db/Danio_rerio/rRNA_db/zebrafish_rRNA \
-t /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alimnaeus/gen_input_fastq/SPORTS/sports1.1/db/Danio_rerio/GtRNAdb/danRer6-tRNAs \
-w /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alimnaeus/gen_input_fastq/SPORTS/sports1.1/db/Danio_rerio/piRBase/piR_dre_v1.0 \
-e /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alimnaeus/gen_input_fastq/SPORTS/sports1.1/db/Danio_rerio/Ensembl/Danio_rerio.GRCz10.ncrna \
-f /disk/bioscratch/Podrab_lab/gazal/sRNA_gazal/align_with_alimnaeus/gen_input_fastq/SPORTS/sports1.1/db/Danio_rerio/Rfam_12.3/Rfam-12.3-zebrafish

This will generate a number of output files, from which the tables generated as a result file *_output.txt for each sample are taken and are used to extract annotation of mapped reads. Top 50 rows of this table are shown for the sample 10_12d_4hrA below.

It also generates a number of plots to categorize the annotations and their length distributions for each sample.

Compiling output files

At the end, we need to compile all the output files into a single dataframe that can be exported to further do downstream processing such as statistics, differential expression, and clustering.

We provide an R script that compiles these output files:

1. Annotation: From sports- *.mapped_output.txt
2. Alignment summary: From .bam files with samtools- *.summary.txt
3. Readcount: From .bam files with samtools and awk- *.readcount.txt
4. Location: From .bam files with bedtools- *.bedtools.txt

In addition, we also need a sample design file (.csv format) that has details about the samples. This file makes it easier to do statistical analysis on the data using R packages like DESeq2, ExpressionSet, and EdgeR.

You would alter the following lines in the R script that basically tells the program where these txt files are located.

parent_dir <- "D:/PSU_work/smrnaseq/mito_map"
samples <- "D:/PSU_work/smrnaseq/mito_map/design_samples.csv"
readcount_txt_pattern <- "sorted.readcount.txt$"
output_txt_pattern <- "mapped_output.txt$"
summary_txt_pattern <- "summary.txt$"
bedtools_txt_pattern <- "bedtools.txt$"

Set pattern of filenames: these files are searched in the parent_dir recursively. Make sure that the parent_dir contains these files and the pattern should be unique to list the files that we need.

Visualizing

We use the alignments with mitochondrial genome to view the aligned reads.

Jbrowse is used to explore alignments.

Files used:

1. Aligned bam files for each sample and its corresponding index file (bam, bai)
2. Reference genome for mitochondria (.fasta) and its index file (.fai)
3. Genome feature file (.gff) for mitochondrial genome

Click to enlarge

This figure shows mitochondiral genome of 100bp length from 17100 to 17199. On this genome, we see 1) the colored basepairs, 2) Genomic features of the mentioned length, 3) Alignments from 4 samples (4d t0 replicates) that show reads aligned to the genome colored by strand while also showing the coverage histogram in grey.